RAG Development
Retrieval systems that cite their sources.
We build production RAG pipelines tuned for your corpus — hybrid search, rerankers, source-grounded citations, and evals that catch silent regressions before users do.
Retrieval latency
<200 ms
P95 for hybrid search + rerank on production workloads
Citation accuracy
95%+
Evaluated against ground-truth answers
Index refresh
Minutes
Incremental updates without full rebuilds
Data sources
Any
PDFs, databases, wikis, APIs, object storage
What our RAG systems include
Hybrid BM25 + vector retrieval
Combine keyword and semantic search so the pipeline finds exact matches and conceptual matches, even with domain terminology.
Cross-encoder rerank
Re-score the top candidates with a dedicated model so the best evidence reaches the language model.
Source-grounded citations
Every generated answer references the exact document, page, or record it came from — easy to verify and audit.
Continuous eval harness
Catch silent regressions in answer quality, retrieval hit-rate, and latency before any change reaches users.
Index freshness pipelines
Incremental ingestion, versioned indexes, and rollback so new documents are searchable quickly and safely.
Permission-aware retrieval
Respect document-level and user-level access controls so agents only see what the caller is allowed to see.
Where teams deploy them first
Internal knowledge base
Employees get cited answers across handbooks, wikis, tickets, and code docs instead of hunting through folders.
Customer support Q&A
Support agents and end users get answers grounded in product docs, policies, and order history.
Legal and compliance research
Search contracts, regulations, and case files with precise citations and audit trails.
Government document services
Citizens and staff query forms, regulations, and registries in natural language with source references.
Custom RAG vs. off-the-shelf retrieval
| Capability | Premium Robots | Generic platform |
|---|---|---|
| Hybrid retrieval | BM25 + vector | Vector only |
| Reranking | Cross-encoder | Cosine similarity |
| Citations | Grounded by default | Optional or missing |
| Eval harness | Continuous | Manual spot checks |
| Index updates | Incremental | Full rebuilds |
Frequently asked questions
- What is RAG?
- Retrieval-Augmented Generation (RAG) is an architecture that fetches the most relevant documents or records for a question, then asks a language model to generate an answer grounded in that retrieved context. It cuts hallucinations and lets the system cite real sources.
- Why does our RAG pipeline need custom tuning?
- Off-the-shelf RAG often fails on real documents: tables, mixed languages, scanned PDFs, and domain jargon. We tune chunking, embeddings, hybrid retrieval, and reranking for your actual corpus so answers are accurate and useful.
- How do you measure answer quality?
- We build a continuous eval harness with metrics like answer relevance, citation accuracy, retrieval hit-rate, and latency. It runs on every index or model change so regressions are caught before users see them.
- Can it connect to our existing data stores?
- Yes. We integrate with Postgres + pgvector, Qdrant, OpenSearch, Elastic, S3, SharePoint, Confluence, and custom APIs. Your documents stay where they are; we build the retrieval layer on top.
- How long does a RAG deployment take?
- A scoped pilot over one document collection is typically live in 3–5 weeks, including ingestion pipeline, retrieval tuning, eval suite, and a feedback loop. Multi-corpus enterprise rollouts run 8–12 weeks.
Scope your RAG pilot
Send us one document collection and we will propose a retrieval pipeline with accuracy and latency targets attached.